Abstract
Sleepiness and fatigue are important risk factors in the transport sector and bio-mathematical sleepiness, sleep and fatigue modeling is increasingly becoming a valuable tool for assessing safety of work schedules and rosters in Fatigue Risk Management Systems (FRMS). The present study sought to validate the inner workings of one such model, Three Process Model (TPM), on aircrews and extend the model with functions to model jetlag and to directly assess the risk of any sleepiness level in any shift schedule or roster with and without knowledge of sleep timings. We collected sleep and sleepiness data from 136 aircrews in a real life situation by means of an application running on a handheld touch screen computer device (iPhone, iPod or iPad) and used the TPM to predict sleepiness with varying level of complexity of model equations and data. The results based on multilevel linear and non-linear mixed effects models showed that the TPM predictions correlated with observed ratings of sleepiness, but explorative analyses suggest that the default model can be improved and reduced to include only two-processes (S+C), with adjusted phases of the circadian process based on a single question of circadian type. We also extended the model with a function to model jetlag acclimatization and with estimates of individual differences including reference limits accounting for 50%, 75% and 90% of the population as well as functions for predicting the probability of any level of sleepiness for ecological assessment of absolute and relative risk of sleepiness in shift systems for safety applications.
Highlights
Air transportation is the safest form of transportation per kilometer travelled [1,2]
The circadian system is a large source of individual differences that may be of particular importance for aircrews that often travel across several time zones and become exposed to jetlag
Overall model fit is summarized in the log likelihood statistics, the residual (e) and subject level random effect (g) standard deviations, but selected deviance statistics (2*2log likelihood difference between two models) are presented for approximate likelihood ratio testing
Summary
Air transportation is the safest form of transportation per kilometer travelled [1,2]. Accidents in aviation rarely have a single cause, and human errors are involved in the majority of them [3]. The link between human error and fatigue has been established in several studies [4]. The main causes of sleepiness and fatigue are 1) circadian phase, 2) time awake, and 3) amount of prior sleep [5,6]. Individual differences are likely to play a role in sleepiness and fatigue related accidents [8], driving performance [9], as well as modify sleep length [10] and performance during sleep deprivation [11]. Individual differences in the circadian type are among the most systematically studied with several rating scales developed to assess an approximate phase in individuals [12,13]
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